Department of Pattern Recognition

Real world materials often change their appearance over time. If these variations are spatially and temporally homogeneous then the material visual appearance can be represented by a dynamic texture which is a natural extension of classic texture concept including the time as an extra dimension. In this article we present possible way to handle multispectral dynamic textures based on a combination of input data eigen analysis and subsequent processing of temporal mixing coefficients. The proposed method exhibits overall good performance, offers extremely fast synthesis which is not restricted in temporal dimension and simultaneously enables to compress significantly the original measured visual data.

The Bidirectional Texture Function (BTF) is the recent most advanced representation of visual properties of surface materials. It specifies their appearance due to varying spatial, illumination, and viewing conditions. Corresponding enormous BTF measurements require a mathematical representation allowing extreme compression but simultaneously preserving its high visual fidelity. We present a novel BTF model based on a set of underlying mono-spectral two-dimensional (2D) moving average factors. A mono-spectral moving average model assumes that a stochastic mono-spectral texture is produced by convolving an uncorrelated 2D random field with a 2D filter which completely characterizes the texture. The BTF model combines several multi-spectral band limited spatial factors, subsequently factorized into a set of mono-spectral moving average representations, and range map to produce the required BTF texture space. This enables very high BTF space compression ratio, unlimited texture enlargement, and reconstruction of missing unmeasured parts of the BTF space.

The Bidirectional Texture Function (BTF) is the recent most advanced representation of material surface visual properties. BTF specifies the changes of its visual appearance due to varying illumination and viewing angles. Such a function might be represented by thousands of images of given material surface. Original data cannot be used due to its size and some compression is necessary. This paper presents a novel probabilistic model for BTF textures. The method combines synthesized smooth texture and corresponding range map to produce the required BTF texture. Proposed scheme enables very high BTF texture compression ratio and may be used to reconstruct BTF space as well.

Abstract. The Bidirectional Texture Function (BTF) is the recent most advanced representation of visual properties of surface materials. It specifies their altering appearance due to varying illumination and viewing conditions. Corresponding huge BTF measurements require a mathematical representation allowing simultaneously extremal compression as well as high visual fidelity. We present a novel Markovian BTF model based on a set of underlying simultaneous autoregressive models (SAR). This complex but efficient BTF-SAR model combines several multispectral band limited spatial factors and range map sub-models to produce the required BTF texture space. The BTF-SAR model enables very high BTF space compression ratio, texture enlargement, and reconstruction of missing unmeasured parts of the BTF space.

Appearance of real scenes is highly dependent on actual conditions as illumination and viewpoint, which significantly complicates automatic analysis of images of such scenes. In this thesis, we introduce novel textural features, which are suitable for robust recognition of natural and artificial materials (textures) present in real scenes. These features are based on efficient modelling of spatial relations by a type of Markov Random Field (MRF) model and we proved that they are invariant to illumination colour, cast shadows, and texture rotation. Moreover, the features are robust to illumination direction and degradation by Gaussian noise, they are also related to human perception of textures.